Competitive Adsorption of Cd(II), Cr(VI), and Pb(II) onto Nanomaghemite: A Spectroscopic and Modeling Approach
Bibliographic record
Abstract
A combined modeling and spectroscopic approach is used to describe Cd(II), Cr(VI), and Pb(II) adsorption onto nanomaghemite and nanomaghemite coated quartz. A pseudo-second order kinetic model fitted the adsorption data well. The sorption capacity of nanomaghemite was evaluated using a Langmuir isotherm model, and a diffuse double layer surface complexation model (DLM) was developed to describe metal adsorption. Adsorption mechanisms were assessed using X-ray photoelectron spectroscopy and X-ray absorption spectroscopy. Pb(II) adsorption occurs mainly via formation of inner-sphere complexes, whereas Cr(VI) likely adsorbs mainly as outer-sphere complexes and Cd(II) as a mixture of inner- and outer-sphere complexes. The simple DLM describes well the pH-dependence of single adsorption edges. However, it fails to adequately capture metal adsorption behavior over broad ranges of ionic strength or metal-loading on the sorbents. For systems with equimolar concentrations of Pb(II), Cd(II), and Cr(VI). Pb(II) adsorption was reasonably well predicted by the DLM, but predictions were poorer for Cr(VI) and Cd(II). This study demonstrates that a simple DLM can describe well the adsorption of the studied metals in mixed sorbate-sorbent systems, but only under narrow ranges of ionic strength or metal loading. The results also highlight the sorption potential of nanomaghemite for metals in complex systems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".